7 Agricultural Wages in India: The Role of Health, Nutrition, and Seasonally

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1 7 Agricultural Wages in India: The Role of Health, Nutrition, and Seasonally JERE R. BEHRMAN AND ANIL B. DEOLALIKAR There are two persistent themes in the development literature. The first is that there are important seasonal variations in nutritional and health status. The second is that there is a technically determined link between nutritional status or health on the one hand and labor effort and productivity on the other. The first theme has been emphasized by Chambers et al. (1981) and Chambers (1982), who contend that seasonal deprivation in nutrient and other health-related intakes and changes in health status are considerable, particularly during the wet season for women and children. Longhurst and Payne (1979) also emphasize that hungry or lean seasons are "an important, if not the most important determinant of nutrition in less developed countries." But as these papers emphasize, there are serious deficiencies in available empirical research on such seasonality. The second theme is often summarized as the "wage efficiency hypothesis" and has been discussed by Leibenstein (1957), Mazumdar (1959), Stiglitz (1976), and Bliss and Stern (1978), and by Alderman and Shan in this book (chapter 6). However, it has been subjected to little systematic empirical testing for a number of reasons. First, the nutritionhealth-productivity relationship cannot be established by mere correlations between variables, since a correlation could be picking up the effect of increased productivity, and thereby income, on nutrition or health, rather than vice versa. A more rigorous test involves regression of productivity indicators on the nutritional or health status of a worker, recognizing that nutrition and health are subject to choice and hence are endogenous variables. Second, the appropriate concept of productivity is marginal, not average, productivity, which is rarely observed directly by social scientists. The measurement of marginal productivity often requires the estimation of a technical production function or the acceptance of the assumption that wages equal marginal products for labor. Third, there may be substantial interpersonal variations in nutrition, health, and productivity, such as those due to seasonality. 107

2 108 Jere R. Behrman and Anil B. Deolalikar No study that we are aware of has addressed all of these problems. There are many that have been oblivious to all of them, 1 though some recent studies by Deolalikar (1988), Strauss (1986), and Sahn and Alderman (forthcoming) do advance the state of the empirical art concerning nutrition-health-productivity relations by direct estimation of production relations for individuals or households within a framework in which the possible simultaneity of nutrition and health with productivity is controlled. All studies estimate agricultural production functions with hired labor and nonlabor inputs in addition to family labor inputs. All find evidence of some health and nutrition effects on labor productivity. All suffer from some deficiencies, including very limited or no exploration of seasonality. In this chapter, we attempt to integrate these two themes in the literature by using a rural South Indian sample to explore how seasonal changes in nutrient intakes and health status affect labor market productivity, as reflected in market wage rates. Our exploration attempts to improve on the earlier literature by treating health and nutrition as simultaneously determined with wage rates and by taking account of seasonal variations in the health-nutrition-productivity relationship. We investigate questions such as the following: Do the impacts of nutrition and health differ between the agricultural peak and slack labor seasons? Is there evidence of a differential impact of nutrient intake flows versus cumulative stock measures of health on labor productivity, as might be suggested by the adaptability hypothesis promoted by Payne and Cutler (1984), Sukhatme (1982), Srinivasan (1981), and others? Do the effects of health, nutrition, and seasonality differ for males versus females? The first section outlines the model underlying our empirical specifications, and the second describes the data set. The final section presents and discusses our empirical results. Model and Estimation There is a long and established tradition of estimating wage equations for developed countries (Mincer 1974). Recently, a number of wage equation studies also have been undertaken for less developed countries (Psacharopoulos 1982, Birdsall and Sabot 1988). For our application to a traditional agricultural setting in this chapter, we extend the standard wage equation framework in three ways. First, we explore the impact, if any, of health and nutrition on individual wage rates. Second, we test whether the parameters of the wage equation vary between the peak and slack agricultural seasons. Third, we treat health and nutrition as endogenous variables. 1. Many of these studies are reviewed in Behrman and Deolalikar 1988b.

3 Agricultural Wages in India 109 Estimated individual wage equations typically include as arguments personal characteristics of an individual such as schooling, labor force experience, and gender. In an agricultural setting in a less developed country, however, it is likely that the labor market also offers a wage premium to healthy and well-nourished workers, since many agricultural tasks tend to demand physical strength or substantial energy expenditures. We therefore include health and nutrition as arguments in the individual wage rate equations that we estimate below, and expect their coefficients to have positive signs. Health and nutrition cannot be treated satisfactorily as exogenous variables influencing wages in the above analysis, since there is substantial evidence suggesting that health and nutrition are choice variables (Behrman and Deolalikar 1987). We therefore treat health and nutrition as endogenous variables in the wage and labor supply equations, and use agricultural consumption and product prices and farm assets as instruments for them (Strauss 1985). Earlier studies, such as those by Ryan (1982) and Strauss (1986), have included measures either of health status or of nutrient intake as explanatory variables in their agricultural production functions or wage equations. However, our review of empirical health production studies in Behrman and Deolalikar (1988b) indicates that current nutrient intakes do not affect stock measures of health, particularly for adults. We argue that such results suggest that short-run changes in nutrient intakes are reflected in changes in energy expenditure rather than in changes in health status. Therefore, we have included measures of both health status and nutrient intake as explanatory variables in our wage equations to allow nutrient intakes to have an additional impact (over and above impact through health status) on labor productivity. Another reason for including both nutrient intake and stock measures of health in the wage equation is that the two may fulfill qualitatively different needs in agricultural operations. Health is associated with innate strength or "horsepower," while current nutrient intakes are associated with energy expenditure. Agricultural tasks vary in their requirements of these two attributes. We estimate forms of the wage equation that allow all coefficients to differ across the agricultural peak and slack seasons. Tasks performed in the two seasons may differ in their requirements of innate strength versus energy expenditure (as well as other attributes such as education), and this would be reflected in differential labor market valuation of these attributes across the two seasons. Wage equations can be estimated only for those individuals participating in the casual daily labor market and hence reporting a wage. The nonrandomness of this sample may result in biased estimates for the wage

4 110 Jere R. Behrman and Anil B. Deolalikar equation, particularly if labor market participation is a choice variable for the individual (Heckman 1976; Olsen 1980). We have corrected for this potential selectivity bias in our estimates by using Olsen's (1980) leastsquares selectivity correction procedure. The wage equations are estimated using OLS-IV estimation methods, with consumption and farm goods prices and farm assets serving as instruments for health and nutrition. 2 The wage equation estimated is semilogarithmic, since this is the functional form that has been most commonly estimated in the literature (Mincer 1974; Psacharopoulos 1982; Birdsall and Sabot 1988). Data We used the ICRISAT VLS (International Crops Research Institute for the Semi-Arid Tropics Village Level Studies) data set to estimate individual wage equations. The ICRISAT VLS data are panel data that have been collected at regular intervals since mid-1975 on production, expenditure, time allocation, prices, and socioeconomic characteristics for 240 households in six carefully selected "typical" villages in three different agroclimatic zones in SAT India. Within each village, 10 households are randomly selected as representatives of agricultural labor and nonlandholding households, and another 30 are a stratified (by size of landholdings) random sample of cultivating households. Data on daily wages received and days worked by participants in the casual agricultural labor market were collected every two to four weeks. Since the precise intervals between interviews vary, the wage data were smoothed and then aggregated into peak and slack seasons. Peak seasons were defined as those months when opportunity costs of labor, defined as the product of wages and the probability of involuntary unemployment, were at a peak. Slack seasons were the remaining months of the year. The peak and slack periods so defined were not congruent across villages, even in the same agroclimatic zone (Ryan, Ghodake, and Sarin 1980). Table 7.1 reports the village-specific peak-period months calculated by Ryan, Ghodake, and Sarin and used in this chapter. For the and agricultural years, four rounds of a spe- 2. Deolalikar (1988) has estimated a wage equation in first differences (but not specific to seasons) with annual data from the same primary data source that is used here. He finds both calorie intake and weight-for-height to be significant determinants of wages. In the present study, we are unable to estimate a fixed-effects model, since this would reduce severely the size of the sample and would require dropping a large number of individuals who did not report wage, health, and nutrition data for all the periods (namely, slack , peak , and peak ).

5 Agricultural Wages in India 111 cial nutrition survey were undertaken by ICRISAT to record individual nutrient intakes in the past 24 hours and anthropometric measures of health status. Since the dates on which each of the rounds was undertaken were available, we were able to aggregate the health and nutrition data into the peak and slack seasons defined above. The health and nutrition data were then merged with the wage data. We use the average seasonal value for each casual labor market participant as an observation, which gives us a total of 468 individual and seasonal observations. We use average daily intake of calories as the relevant measure of nutrient intake because calories are widely recognized to be the most important nutrient. For health status, we use weight-for-height, an anthropometric measure that is widely assumed to reflect short-run health status. We use the measures in their original units (calories and kilograms/centimeter) as well as in age- and sex-standardized units (with the Indian recommended daily allowances in Gopalan, Sastry, and Balasubramanian [1971] serving as the standard for calorie consumption and the modified Harvard standard in Ryan et al. [1984] serving as the standard for weightfor-height). Table 7.2 gives the means and standard deviations by season and sex for the wage rate and for health and nutrition variables. One fact that immediately stands out is that agricultural wage rates do not vary much across the peak and slack seasons. Female wage rates are only 6.5 percent higher and male wages only 2 percent higher in the peak than in the slack season. Neither of these differences is statistically significant. The relative constancy of wage rates across seasons is largely the result of geographical mobility of labor in rural South India. In the peak season, migrant labor from other regions prevents wage rates from increasing, while in the slack season, out-migration of local labor keeps wage rates from falling significantly. 3 The observed constancy of agricultural wages across seasons, however, may hide large interseasonal variations in the relationship between health and nutrition on the one hand and wage rates on the other. Like wage rates, weight-for-height is also largely invariant across peak and slack seasons for both males and females, as is calorie intake for females. For males, however, daily calorie intake is higher (by about 12 percent) in the peak than in the slack season, which suggests that the additional calories consumed by males in the peak season may go toward satisfying the greater energy requirements in physically demanding, peakseason male tasks, such as ploughing. 3. However, as noted above with reference to the definitions of the seasons, employment probabilities are higher in the peak than in the slack season, so expected wages are higher in the peak season.

6 TABLE 7.1 Peak and slack labor periods for adults in six SAT villages of peninsular India, District and Village Category Months Peak Period Major Operations Slack Period Months Mahbubnager Aurepalle Dokur Males and females Males and females Dec-Jan Nov-Jan Harvesting and threshing sorghum, pearl millet, castor Harvesting and threshing sorghum, nursery bed preparation, paddy transplanting Feb-Apr Feb-June Sholapur Shirapur Kalman Males Females Males Females Apr-May July-Aug Sep Dec-Feb Jan-Mar May Mar-Apr Nov Preparatory tillage, ploughing Sowing pearl millet, mesta, mung bean Harvesting and threshing pearl millet, mesta, mung bean Sowing and harvesting wheat, sorghum, chickpea, safflower Harvesting and threshing wheat, sorghum, chickpea, safflower Preparatory tillage, ploughing Harvesting and threshing wheat, sorghum, chickpea, safflower Harvesting and threshing pearl millet, mesta Dec, Feb-Mar Apr-Aug Aug-Oct Oct-Dec, Jan

7 Akola Kanzara Kinkheda Males Mar Harvesting cotton; harvesting Apr-Sep and threshing pigeon pea Aug-Sep Preparatory tillage, sowing wheat, chickpea; harvesting sorghum, groundnut Females Oct-Dec Harvesting and threshing Apr-June sorghum, groundnut, cotton Mar Harvesting cotton Males Apr Preparatory tillage Aug-Oct, June-July Sowing, interculturing May cotton, sorghum, pigeon pea, mung bean Nov-Dec Harvesting and threshing sorghum, groundnut; sowing wheat and chickpea Females May Field cleaning Feb-Mar Sep-Dec Harvesting and threshing sorghum, groundnut; weeding cotton SOURCE: Adapted from Ryan, Chodake, and Sarin 1980, pp

8 114 Jere R. Behrman and Anil B. Deolalikar TABLE 7.2 Means and standard deviations by season, SAT India, Slack Season Peak Season Daily wage rate (rupees) Men Women Average daily calorie intake Men Women Weight-for-height Men Women Years of schooling Men Women Years of experience" Men Women (kg/cm) 3.34 (1.08) 2.01 (0.55) 2,123 (920) 2,053 (850) (0.07) (0.06) 2.5 (3.0) 0.8 (2.2) 19.8 (15.2) 22.9(17.1) 3.41 (0.94) 2.14 (0.62) 2,373 (880) 2,035 (784) (0.07) (0.06) NOTE: Figures in parentheses are standard deviations. "Experience is defined as age minus years of schooling minus 8. See Ryan and Wallace Empirical Results Table 7.3 presents the estimates for the wage equation estimated over all market participants as well as separately for male and female market participants. Since the peak-season slope dummies were uniformly insignificant at the 10 percent level in all the equations for all right-side variables other than calories and weight-for-height, we allow only the intercept terms and the coefficients on calories and weight-for-height to differ across seasons in the results reported in this table. In addition, since the use of unstandardized versus standardized values of calories and weight-forheight made virtually no difference in our results, we report only the estimates using unstandardized calories and weight-for-height. Finally Olsen's sample selectivity correction procedure did not indicate sample selectivity as a problem in any of the three wage equations. Therefore, we present only the estimates in which no correction for selectivity is made in table 7.3. The OLS-IV estimates for all labor force participants in table 7.3 (column 2) indicate significantly (at the 10 percent level) higher wage rates (by about 5 percent) in the peak than in the slack season, holding all other wage-determining factors constant. In the slack season, calorie intake is not a significant determinant of wages, although weight-for-height is (with an elasticity at the sample means of 0.67). However, calorie intake has a

9 Agricultural Wages in India 115 TABLE 7.3 Agricultural semilogarithmic wage equations, SAT India, Independent Variables Intercept Peak season" Calories* 1 -" Calories X peak season b ' c Weight-for-height b Weight-for-height X peak season Schooling All Participants Male Participants Female Participants OLS (1) (0.20) (0.00) (0.20) (1.50) (3.70) (0.40) (3.10) Experience (2.70) Experience-squared (2.70) Male" (17.00) OLS-IV (2) (1.00) (1.80) (0.60) (2.40) (3.00) (2.30) (3.90) (4.90) (4.10) (17.80) OLS (3) (0.04) (1.00) (0.80) (1. 10) (4.70) (0.40) (4. 50) (3. 60) (2. 30) OLS-IV (4) -0.,181 (0.50) (0.10) (1. 50) (1. 50) (2.10) (1. 80) (5. 70) (6. 30) (3. 80) OLS (5) (2.50) (0.30) (2.00) (0.60) (0.40) (0.80) (0.30) (0.10) (1.00) OLS-IV (6) (2.40) (1.60) (0. 80) (1. 20) (0. 80) (0. 50) (0. 40) (0.90) (1. 40) R 2 F-ratio No. of observations Residual sum of squares NOTE: Figures in parentheses are absolute f-ratios. A set of four caste dummies also were included in each of the above equations. Since none of the caste coefficients was significant, they have not been reported here. Dichotomous variable with value of one in indicated state and zero otherwise. Endogenous variable. Instruments used in the instrumental variable estimates (OLS-IV) were the prices of milk, sorghum, rice, pulses, and sugar; farm size (in acres); and the percentage of cultivated area under superior soil. C A11 coefficients in this row have been multiplied by significantly larger effect on individual wage rates in the peak than in the slack season (with an elasticity of 0.27 in the peak season), while weightfor-height has a significantly smaller effect on wages in the peak season (with an elasticity of 0.35 in the peak season). Thus the roles of nutrient intake and health status seem to reverse between the two seasons: while calorie intake is much more important in determining wages (and therefore, we assume, marginal productivity) in the peak than in the slack season, weight-for-height appears to be more important in the slack than in

10 116 Jere R. Behrman and Anil B. Deolalikar the peak season. (None of the four effects is observed to be significant and negative.) One possible explanation for this finding may lie in the nature of the tasks performed in the two seasons. Tasks normally performed in the peak season, such as harvesting (see table 7.1), may require greater sustained human energy expenditure than slack-season tasks, but may not require innate strength (which is associated with greater weight-for-height) to the same extent as in the slack season. In fact small size may be a distinct advantage in certain peak-season tasks, such as harvesting and transplanting. Interestingly, both schooling and experience are significant (at the 5 percent level) determinants of individual wage rates, even in the context of agricultural labor activities. The returns to schooling implied by our estimates are low (about 2 percent), however, compared with most estimates obtained in other studies of urban areas and nonagricultural activities (Psacharopoulos 1982; Birdsall and Sabot 1988). Our estimate indicates diminishing returns to experience, with a return of less than 1 percent for each additional year of experience at the sample mean. None of the dichotomous variables for caste is significant, while gender is by far the most significant determinant of wages (with males earning about 45 percent higher wage rates than females, holding other factors constant). For male participants, the wage equation results remain broadly similar to those discussed above, though calories in the peak season have a significant impact only at the 20 percent level, and the returns to schooling and to experience are higher than in the relation for all participants. For female participants, however, the wage equation results are quite different. With the exception of the peak-season intercept dummy, which is significantly positive at the 10 percent level, none of the explanatory variables in the wage equation is significant. Schooling, experience, calorie intake, and weight-for-height do not appear to matter at all in female wage determination in either the slack or the peak season. An explanation for this result lies in the segmentation between male and female tasks observed by Ryan and Ghodake (1984) for the same sample used here. They found that, of a total of 16 agricultural operations, only 2 harvesting and threshing used considerable amounts of both male and female labor. Five tasks nursery bed raising, transplanting, planting, weeding, and thinning were almost exclusively performed by female labor, while male labor was almost exclusively used for the remaining nine operations, four of them involving bullock power. If male labor generally performs more physically demanding tasks than female labor, it is easy to understand why it receives a premium in wages as a result of better health and nutrition and why female labor does not receive such a premium.

11 Agricultural Wages in India 117 Our results thus indicate significant support for the wage efficiency hypothesis for males within the rural SAT Indian context of this study, with significant differences between the peak and slack seasons. Food consumption, at least of adult males, has an immediate productivity impact through energy availabilities and a possible longer-run impact through health status. This implies that policies that improved males' health and nutrition would result in productivity improvements within this context. It also means that households might rationally allocate somewhat more food relative to requirements to adult males, especially during the peak season. There is the further implication that male wages should be treated as endogenous in an analysis of the determinants of other outcomes for example, investment in children or in physical assets for these households. With the current labor force division by gender and the extent of nutrition and health variation experienced in the sample, however, such results do not carry over to women.